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torch.optim.lr_scheduler.StepLR Class Reference
Inheritance diagram for torch.optim.lr_scheduler.StepLR:
torch.optim.lr_scheduler._LRScheduler test_optim.LegacyStepLR

Public Member Functions

def __init__ (self, optimizer, step_size, gamma=0.1, last_epoch=-1)
def get_lr (self)
- Public Member Functions inherited from torch.optim.lr_scheduler._LRScheduler
def __init__ (self, optimizer, last_epoch=-1)
def state_dict (self)
def load_state_dict (self, state_dict)
def get_lr (self)
def step (self, epoch=None)

Public Attributes

- Public Attributes inherited from torch.optim.lr_scheduler._LRScheduler

Detailed Description

Decays the learning rate of each parameter group by gamma every
step_size epochs. Notice that such decay can happen simultaneously with
other changes to the learning rate from outside this scheduler. When
last_epoch=-1, sets initial lr as lr.

    optimizer (Optimizer): Wrapped optimizer.
    step_size (int): Period of learning rate decay.
    gamma (float): Multiplicative factor of learning rate decay.
        Default: 0.1.
    last_epoch (int): The index of last epoch. Default: -1.

    >>> # Assuming optimizer uses lr = 0.05 for all groups
    >>> # lr = 0.05     if epoch < 30
    >>> # lr = 0.005    if 30 <= epoch < 60
    >>> # lr = 0.0005   if 60 <= epoch < 90
    >>> # ...
    >>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
    >>> for epoch in range(100):
    >>>     scheduler.step()
    >>>     train(...)
    >>>     validate(...)

Definition at line 126 of file

The documentation for this class was generated from the following file: